Managing HPC, GPU, and Lab Infrastructure Without a Big IT Team
Your compute cluster and lab benches are research instruments, not a help desk ticket, and they should be run that way.
Lab infrastructure is its own animal
A standard MSP playbook assumes laptops, email, and a few servers. Hard-tech reality is a GPU cluster, a job scheduler, instrument PCs running software that cannot be patched on a whim, and benches full of equipment that talks over old protocols and serial ports. Treating that like an office network gets you downtime and angry researchers.
The goal is uptime and reproducibility for the people doing the science, with enough structure that a failed node or a corrupted dataset does not erase a week of work.
Get scheduling and utilization under control
Expensive GPUs sit idle when access is ad hoc and one team quietly hoards a box. A real scheduler, whether Slurm on metal or a managed queue in the cloud, turns contention into a fair queue and gives you the usage data to justify the next purchase.
Decide deliberately what stays on-premises and what bursts to the cloud. Steady, heavy training often pays to own; spiky demand and short experiments are usually cheaper to rent. The answer is a mix, revisited as the numbers move.
Treat instrument PCs as fragile and isolated
The machine driving a mass spec or a wafer prober often runs an operating system you are not allowed to touch and software the vendor will not support if you change anything. Do not fight that. Segment those machines onto their own network, control what they can reach, and protect them with backups and tight access instead of aggressive patching.
That isolation is also your security story for the lab: an instrument that cannot browse the web or reach the internet is a much smaller target.
Make data and access boringly reliable
Research data needs versioned, tested backups and a clear home, so a deleted directory or a dead drive is a restore, not a catastrophe. Lab access should follow the same identity rules as the rest of the company, with named accounts instead of one shared login on a sticky note.
You do not need a large internal team for any of this. You need someone who understands schedulers, storage, and lab realities to set it up and keep it humming while your engineers stay on the actual research.